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from typing import Optional, Tuple, Union
from functools import partial
import torch
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.processing_utils import Unpack
from transformers.utils import logging
from transformers import AutoModel
from transformers.models.mistral.configuration_mistral import MistralConfig
from transformers.models.mistral.modeling_mistral import MistralModel
from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa
from .configuration_mistral_dual import MistralDualConfig
logger = logging.get_logger(__name__)
class MistralDualModel(MistralModel):
config_class = MistralDualConfig
def __init__(self, config: MistralDualConfig):
super().__init__(config)
for layer in self.layers:
layer.self_attn.is_causal = False
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
is_causal = False,
**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if use_cache and past_key_values is None:
past_key_values = DynamicCache()
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
# print(causal_mask)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
partial(decoder_layer.__call__, is_causal=is_causal),
hidden_states,
causal_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
cache_position,
position_embeddings,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
is_causal=is_causal,
**flash_attn_kwargs,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
output = BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values if use_cache else None,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
return output if return_dict else output.to_tuple()
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
config: MistralConfig,
past_key_values: Cache,
):
"""
Creates a bidirectional 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`,
where all tokens can attend to all others.
"""
if attention_mask is not None and attention_mask.dim() == 4:
return attention_mask # Already in correct shape
min_dtype = torch.finfo(dtype).min
# Create a full attention mask allowing all tokens to attend to all others
bidirectional_mask = torch.zeros((sequence_length, target_length), dtype=dtype, device=device)
bidirectional_mask = bidirectional_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
bidirectional_mask = bidirectional_mask.clone() # Ensure contiguous memory for in-place edit
if attention_mask.shape[-1] > target_length:
attention_mask = attention_mask[:, :target_length]
mask_length = attention_mask.shape[-1]
padding_mask = bidirectional_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
bidirectional_mask[:, :, :, :mask_length] = bidirectional_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return bidirectional_mask
AutoModel.register(MistralDualConfig, MistralDualModel)
MistralDualModel.register_for_auto_class() |